Several competitive techniques have been developed for recognizing spoken commands in various voice assistant applications (e.g., intelligent home control). On the one hand, DNN-based acoustic models often achieve over \(90\%\)  accuracies for resource-sufficient languages (e.g., English [1]}). On the other hand, statistical learning (e.g., kernel SVM [2]}) has been proven to be a viable solution for low-resource languages [3]}, such as Sinhala and Tamil. Moreover, prototypical network based metric learning [4]}, [5]} shows competitive performances in few-shot speaker identification tasks [5]}, [7]}, [8]}, where the model [4]} learns per-class weights for target keywords, as in classification objectives and maximizes the distance between non-targeted samples.
In this work, we will show that such an efficient non-parametric learning approach can be combined with quantum computation for low-resource spoken command recognition.
